identical

R Base 3.6.2

The safe and reliable way to test two objects for being exactly equal. It returns TRUE in this case, FALSE in every other case.

Syntax

identical(x, y, num.eq = TRUE, single.NA = TRUE, attrib.as.set = TRUE,
          ignore.bytecode = TRUE, ignore.environment = FALSE,
          ignore.srcref = TRUE)

Arguments

Parameter	Description
`x, y`	any R objects.
`num.eq`	logical indicating if (double and complex non-NA) numbers should be compared using == (‘equal’), or by bitwise comparison. The latter (non-default) differentiates between -0 and +0.
`single.NA`	logical indicating if there is conceptually just one numeric NA and one NaN; single.NA = FALSE differentiates bit patterns.
`attrib.as.set`	logical indicating if attributes of x and y should be treated as unordered tagged pairlists (“sets”); this currently also applies to slots of S4 objects. It may well be too strict to set attrib.as.set = FALSE.
`ignore.bytecode`	logical indicating if byte code should be ignored when comparing closures.
`ignore.environment`	logical indicating if their environments should be ignored when comparing closures.
`ignore.srcref`	logical indicating if their "srcref" attributes should be ignored when comparing closures.

Return Value

A single logical value, TRUE or FALSE, never NA and never anything other than a single value.

Details

A call to identical is the way to test exact equality in if and while statements, as well as in logical expressions that use && or ||. In all these applications you need to be assured of getting a single logical value. Users often use the comparison operators, such as == or !=, in these situations. It looks natural, but it is not what these operators are designed to do in R. They return an object like the arguments. If you expected x and y to be of length 1, but it happened that one of them was not, you will not get a single FALSE. Similarly, if one of the arguments is NA, the result is also NA. In either case, the expression if(x == y).... won't work as expected. The function all.equal is also sometimes used to test equality this way, but was intended for something different: it allows fo

Examples

Example

# NOT RUN {
identical(1, NULL) ## FALSE -- don't try this with ==
identical(1, 1.)   ## TRUE in R (both are stored as doubles)
identical(1, as.integer(1)) ## FALSE, stored as different types

x <- 1.0; y <- 0.99999999999
## how to test for object equality allowing for numeric fuzz :
(E <- all.equal(x, y))
isTRUE(E) # which is simply defined to just use
identical(TRUE, E)
## If all.equal thinks the objects are different, it returns a
## character string, and the above expression evaluates to FALSE

## even for unusual R objects :
identical(.GlobalEnv, environment())

### ------- Pickyness Flags : -----------------------------

## the infamous example:
identical(0., -0.) # TRUE, i.e. not differentiated
identical(0., -0., num.eq = FALSE)
## similar:
identical(NaN, -NaN) # TRUE
identical(NaN, -NaN, single.NA = FALSE) # differ on bit-level

### For functions ("closure"s): ----------------------------------------------
###     ~~~~~~~~~
f <- function(x) x
f
g <- compiler::cmpfun(f)
g
identical(f, g)                        # TRUE, as bytecode is ignored by default
identical(f, g, ignore.bytecode=FALSE) # FALSE: bytecode differs

## GLM families contain several functions, some of which share an environment:
p1 <- poisson() ; p2 <- poisson()
identical(p1, p2)                          # FALSE
identical(p1, p2, ignore.environment=TRUE) # TRUE

## in interactive use, the 'keep.source' option is typically true:
op <- options(keep.source = TRUE) # and so, these have differing "srcref" :
f1 <- function() {}
f2 <- function() {}
identical(f1,f2)# ignore.srcref= TRUE : TRUE
identical(f1,f2,  ignore.srcref=FALSE)# FALSE
options(op) # revert to previous state

# }